Visualization of Brain Activities of Single-Trial and Averaged Multiple-Trials MEG Data(Neuro, Fuzzy, GA)(<Special Section>Nonlinear Theory and its Applications)
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概要
- 論文の詳細を見る
Treating an averaged multiple-trials data or non-averaged single-trial data is a main approach in recent topics on applying independent component analysis (ICA) to neurobiological signal processing. By taking an average, the signal-to- noise ratio (SNR) is increased but some important information such as the strength of an evoked response and its dynamics will be lost. The single-trial data analysis, on the other hand, can avoid this problem but the SNR is very poor. In this study, we apply ICA to both non-averaged single-trial data and averaged multiple-trials data to determine the properties and advantages of both. Our results show that the analysis of averaged data is effective for seeking the response and dipole location of evoked fields. The non-averaged single-trial data analysis efficiently identifies the strength and dynamic component such as α-wave. For determining both the range of evoked strength and dipole location, an analysis of averaged limited-trials data is better option.
- 社団法人電子情報通信学会の論文
- 2003-09-01
著者
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Arai Takayuki
Department of Electrical and Electronics Engineering, Sophia University
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CAO Jianting
Department of Electrical and Electronics Engineering, Sophia University
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TAKEDA Tsunehiro
Department of Complexity of Science and Engineering, Graduate School of Tokyo University
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Cao Jianting
Department Of Electrical And Electronics Engineering Sophia University
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Arai Takayuki
Department Of Chemistry Faculty Of Engineering Gunma University
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Takeda Tsunehiro
Department Of Complexity Of Science And Engineering Graduate School Of Tokyo University
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Arai T
Sophia Univ. Tokyo Jpn
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KONNO Yoshio
Department of Electrical and Electronics Engineering, Sophia University
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